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I am trying to write a .cpp that takes an input vector and outputs a two-column dataframe with all possible combinations from the input vector. My output gives the desired values, but not as a dataframe. What do I change in the .cpp file to get a dataframe output?
My possible_combos.cpp file looks like this:
#include <Rcpp.h>
using namespace Rcpp;
// [[Rcpp::export]]
GenericVector C_all_combos(GenericVector a) {
int vec_length = a.size();
int vec_length_sq = vec_length*vec_length;
GenericVector expand_vector_a(vec_length_sq);
GenericVector expand_vector_b(vec_length_sq);
for (int i=0; i<vec_length_sq; i++) { expand_vector_a[i] = a[i / vec_length]; };
for (int i=0; i<vec_length_sq; i++) { expand_vector_b[i] = a[i % vec_length]; };
DataFrame my_df = DataFrame::create(Named("v_1") = expand_vector_a,
Named("v_2") = expand_vector_b);
return my_df;
}
/*** R
C_all_combos(c(1, "Cars", 2.3))
*/
The desired output from running Rcpp::sourceCpp("possible_combos.cpp") is:
v_1 v_2
1 1
1 Cars
1 2.3
Cars 1
Cars Cars
Cars 2.3
2.3 1
2.3 Cars
2.3 2.3
But what I get is:
v_1..1. v_1..1..1 v_1..1..2 v_1..Cars. v_1..Cars..1 v_1..Cars..2 v_1..2.3. v_1..2.3..1 v_1..2.3..2
1 1 1 1 Cars Cars Cars 2.3 2.3 2.3
v_2..1. v_2..Cars. v_2..2.3. v_2..1..1 v_2..Cars..1 v_2..2.3..1 v_2..1..2 v_2..Cars..2 v_2..2.3..2
1 1 Cars 2.3 1 Cars 2.3 1 Cars 2.3
Thanks for any tips! I'm familiar with excellent R functions like expand.grid(), but want to experiment with alternatives.
244
The main issue is that Rcpp::GenericVector is a list so the behavior is consistent with R. I show this below and a solution which has a special case for each type of input using a template function
#include <Rcpp.h>
using namespace Rcpp;
// essentially your code
// [[Rcpp::export]]
DataFrame C_all_combos(GenericVector a) {
size_t const vec_length = a.size(),
vec_length_sq = vec_length * vec_length;
GenericVector expand_vector_a(vec_length_sq),
expand_vector_b(vec_length_sq);
for (size_t i = 0; i < vec_length_sq; i++){
expand_vector_a[i] = a[i / vec_length];
expand_vector_b[i] = a[i % vec_length];
}
return DataFrame::create(_["v_1"] = expand_vector_a,
_["v_2"] = expand_vector_b,
_["stringsAsFactors"] = false);
}
// template function used in the new solution
template<class T>
DataFrame C_all_combos_gen(T a) {
size_t const vec_length = a.size(),
vec_length_sq = vec_length * vec_length;
T expand_vector_a(vec_length_sq),
expand_vector_b(vec_length_sq);
for (size_t i = 0; i < vec_length_sq; i++){
expand_vector_a[i] = a[i / vec_length];
expand_vector_b[i] = a[i % vec_length];
}
return DataFrame::create(_["v_1"] = expand_vector_a,
_["v_2"] = expand_vector_b,
_["stringsAsFactors"] = false);
}
// export particular versions
// [[Rcpp::export]]
DataFrame C_all_combos_int(IntegerVector a){
return C_all_combos_gen<IntegerVector>(a);
}
// [[Rcpp::export]]
DataFrame C_all_combos_char(CharacterVector a){
return C_all_combos_gen<CharacterVector>(a);
}
// [[Rcpp::export]]
DataFrame C_all_combos_num(NumericVector a){
return C_all_combos_gen<NumericVector>(a);
}
// [[Rcpp::export]]
DataFrame C_all_combos_log(LogicalVector a){
return C_all_combos_gen<LogicalVector>(a);
}
We can now run the following R code which
R.######
# the issue with your code. Repeat your call
C_all_combos(c(1, "Cars", 2.3))
#R> v_1..1. v_1..1..1 v_1..1..2 v_1..Cars. v_1..Cars..1 v_1..Cars..2 v_1..2.3. v_1..2.3..1 v_1..2.3..2 v_2..1. v_2..Cars. v_2..2.3. v_2..1..1 v_2..Cars..1 v_2..2.3..1 v_2..1..2
#R> 1 1 1 1 Cars Cars Cars 2.3 2.3 2.3 1 Cars 2.3 1 Cars 2.3 1
#R> v_2..Cars..2 v_2..2.3..2
#R> 1 Cars 2.3
# amounts to doing the following in R which yields the same
all_combs <- expand.grid(v_1 = c(1, "Cars", 2.3), v_2 = c(1, "Cars", 2.3),
stringsAsFactors = FALSE)
data.frame(v_1 = as.list(all_combs$v_2),
v_2 = as.list(all_combs$v_1))
#R> v_1..1. v_1..1..1 v_1..1..2 v_1..Cars. v_1..Cars..1 v_1..Cars..2 v_1..2.3. v_1..2.3..1 v_1..2.3..2 v_2..1. v_2..Cars. v_2..2.3. v_2..1..1 v_2..Cars..1 v_2..2.3..1 v_2..1..2
#R> 1 1 1 1 Cars Cars Cars 2.3 2.3 2.3 1 Cars 2.3 1 Cars 2.3 1
#R> v_2..Cars..2 v_2..2.3..2
#R> 1 Cars 2.3
######
# here is a solution with the template function
C_all_combos_R <- function(a){
if(is.logical(a))
return(C_all_combos_log(a))
else if(is.integer(a))
return(C_all_combos_int(a))
else if(is.numeric(a))
return(C_all_combos_num(a))
else if(is.character(a))
return(C_all_combos_char(a))
stop("C_all_combos_R not implemented")
}
# it works
C_all_combos_R(c(1, "Cars", 2.3))
#R> v_1 v_2
#R> 1 1 1
#R> 2 1 Cars
#R> 3 1 2.3
#R> 4 Cars 1
#R> 5 Cars Cars
#R> 6 Cars 2.3
#R> 7 2.3 1
#R> 8 2.3 Cars
#R> 9 2.3 2.3
You can also do all the type checking in C++, avoid the expensive integer division and modulus operation, and avoid the DataFrame constructor like AEF like this
#include <Rcpp.h>
using namespace Rcpp;
template<int T>
SEXP C_all_combos_gen_two(Vector<T> a) {
size_t const vec_length = a.size(),
vec_length_sq = vec_length * vec_length;
Vector<T> expand_vector_a(vec_length_sq),
expand_vector_b(vec_length_sq);
size_t i(0L);
for(size_t jj = 0L; jj < vec_length; ++jj)
for(size_t ii = 0L; ii < vec_length; ++i, ++ii){
expand_vector_a[i] = a[jj];
expand_vector_b[i] = a[ii];
}
List out = List::create(_["v_1"] = expand_vector_a,
_["v_2"] = expand_vector_b);
out.attr("class") = "data.frame";
out.attr("row.names") = Rcpp::seq(1, vec_length_sq);
return out;
}
// [[Rcpp::export]]
SEXP C_all_combos_cpp(SEXP a){
switch( TYPEOF(a) ){
case INTSXP : return C_all_combos_gen_two<INTSXP>(a);
case REALSXP: return C_all_combos_gen_two<REALSXP>(a);
case STRSXP : return C_all_combos_gen_two<STRSXP>(a);
case LGLSXP : return C_all_combos_gen_two<LGLSXP>(a);
case VECSXP : return C_all_combos_gen_two<VECSXP>(a);
default: Rcpp::stop("C_all_combos_cpp not implemented");
}
return DataFrame();
}
The new version yields
C_all_combos_cpp(c(1, "Cars", 2.3))
#R> v_1 v_2
#R> 1 1 1
#R> 2 1 Cars
#R> 3 1 2.3
#R> 4 Cars 1
#R> 5 Cars Cars
#R> 6 Cars 2.3
#R> 7 2.3 1
#R> 8 2.3 Cars
#R> 9 2.3 2.3
and it is fast compared with AEF's solution
C_all_combos_cpp(c(1, "Cars", 2.3))
options(digits = 3)
library(bench)
mark(C_all_combos_cpp = C_all_combos_cpp(c(1, "Cars", 2.3)),
AEF = C_all_combos_aef(c(1, "Cars", 2.3)), check = FALSE)
#R> # A tibble: 2 x 13
#R> expression min median `itr/sec` mem_alloc `gc/sec` n_itr n_gc total_time
#R> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl> <int> <dbl> <bch:tm>
#R> 1 C_all_combos_cpp 4.05µs 5.49µs 169097. 6.62KB 16.9 9999 1 59.1ms
#R> 2 AEF 15.76µs 16.96µs 57030. 2.49KB 45.7 9992 8 175.2ms
larger_num <- rnorm(100)
mark(C_all_combos_cpp = C_all_combos_cpp(larger_num),
AEF = C_all_combos_aef(larger_num), check = FALSE)
#R> # A tibble: 2 x 13
#R> expression min median `itr/sec` mem_alloc `gc/sec` n_itr n_gc total_time
#R> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl> <int> <dbl> <bch:tm>
#R> 1 C_all_combos_cpp 30.9µs 37.7µs 20817. 198KB 88.0 6862 29 330ms
#R> 2 AEF 167.9µs 178.4µs 5558. 199KB 21.5 2585 10 465ms
For completeness, here is the extra C++ code
// [[Rcpp::export]]
SEXP C_all_combos_aef(GenericVector a) {
int vec_length = a.size();
int vec_length_sq = vec_length * vec_length;
GenericVector expand_vector_a(vec_length_sq);
GenericVector expand_vector_b(vec_length_sq);
for (int i=0; i<vec_length_sq; i++) { expand_vector_a[i] = a[i / vec_length]; };
for (int i=0; i<vec_length_sq; i++) { expand_vector_b[i] = a[i % vec_length]; };
List my_df = List::create(Named("v_1") = expand_vector_a,
Named("v_2") = expand_vector_b);
my_df.attr("class") = "data.frame";
my_df.attr("row.names") = Rcpp::seq(1, vec_length_sq);
return my_df;
}
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